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e87dc49 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # %%
import h2o
# %%
h2o.__version__
# %%
h2o.init()
# %%
from h2o.estimators import H2OGradientBoostingEstimator
h2o.init(jvm_custom_args=["sys.ai.h2o.debug.allowJavaVersions", "18"])
# Import the prostate dataset into H2O:
prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
# Set the predictors and response; set the factors:
prostate["CAPSULE"] = prostate["CAPSULE"].asfactor()
predictors = ["ID","AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"]
response = "CAPSULE"
# Build and train the model:
pros_gbm = H2OGradientBoostingEstimator(nfolds=5,
seed=1111,
keep_cross_validation_predictions = True)
pros_gbm.train(x=predictors, y=response, training_frame=prostate)
# Eval performance:
perf = pros_gbm.model_performance()
# Generate predictions on a test set (if necessary):
pred = pros_gbm.predict(prostate)
# Extract feature interactions:
feature_interactions = pros_gbm.feature_interaction()
# %%
feature_interactions
# %%
#save model
h2o.save_model(model=pros_gbm, force=True)
# %%
pros_gbm.save_mojo('mojo')
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